Digital cameras use photosensor arrays to capture images. During image capture, an image is focused on a photosensor array, and individual photo-receptive elements of the array detect photons.
A high signal-to-noise ratio (SNR) is desirable. Sensor signal strength is proportional to the amount of detected photons. Sources of the noise include sensor shot noise and readout noise. Image quality can suffer from an extremely low SNR.
A weak sensor signal can result from underexposure during image capture (due to a lack of detected photons). Underexposure can occur due to poor lighting conditions, incorrect combinations of aperture and shutter speed, etc. Normally, noise in an underexposed image would not be visible because the details in the image are too dark to see. If contrast of the underexposed image is enhanced, however, the noise is amplified as a by-product of the contrast enhancement. As a result, the contrast-enhanced image has a very noisy appearance.
Sensor signal strength can be reduced by reducing the size of the photo-receptive elements. The current trend in digital cameras is to increase image resolution by reducing the size of the photo-receptive elements and increasing pixel count. Once, the common consumer-level camera had a two megapixel array. Now, the common consumer-level camera has a five megapixel array. Soon, the common consumer-level camera will have an eight megapixel array. Despite the higher image resolution, SNR is reduced dramatically.
Low SNR is also likely in images captured by inexpensive photosensor arrays. Inexpensive photosensor arrays and optics are becoming common in cellular phones and personal digital assistants (PDAs). These image capture systems are not intended to capture high quality images, but rather unplanned casual shots, often in poor lighting conditions.
It would be desirable to reduce image noise resulting from low SNR. It would be especially desirable to use a denoising algorithm that does not require a lot of memory and is not computationally intensive, so that the denoising algorithm could be incorporated into processing pipelines of commercial imaging devices (e.g., cameras, printers).